This study aimed at evaluating the effect of different types of external Human-Machine Interfaces (eHMIs) on cyclists’ crossing behaviour when interacting with an automated vehicle (AV) in a shared space. Using a Virtual Reality (VR) experiment, four eHMI designs (no eHMI, textual, symbolic and lights) were systematically tested throughout a series of crossing scenarios, in which participants engaged in the role of a cyclist using a VR bicycle simulator. Each design was tested under yielding and non-yielding conditions. Cyclist behaviour was recorded through objective measures, such as crossing initiation time, gazing time and crossing intentions, while subjective perceptions were captured in a post-experiment questionnaire. The results indicate that eHMIs do influence cyclists’ crossing behaviour, however not consistently across all behavioural metrics. The textual and light eHMI significantly decreased the crossing initiation time of cyclists when the AV is yielding, while the lights increased the crossing time and gazing time when the AV is not yielding. In contrast, any form of eHMI shows a significant improvement across all subjective measures, enhancing the perceived safety, trust and decision-making of cyclists. The symbolic eHMI consistently received the highest ratings, however, overall differences were minimal. Therefore, a gap is observed between the subjective perceptions and impressions of cyclists and their actual behaviour. This study contributes to the growing literature on cyclist-AV interactions, which is still underwhelming. It forms a solid basis for further research optimizing eHMI design for real-world implications. In addition, it proves that VR is an effective tool for analyzing these interactions in realistic yet controlled environments.

Advanced Driver Assistance Systems (ADAS) play a crucial role in enhancing road safety and driver comfort by providing real-time alerts and support. However, the effectiveness of ADAS signals in influencing driver situational awareness (SA) remains an area requiring further investigation. This study examines how different ADAS speed limit alert designs impact the three levels of SA: perception, comprehension, and projection.

A real-world driving experiment was conducted in Amersfoort along a 4.3 km route, comparing two ADAS systems with distinct signal designs. System A provided both the current and upcoming speed limits, while System B displayed only the current limit. Data collection included eye-tracking for SA1 (perception), self-reported questionnaires for SA2 (comprehension), and speed analysis for SA3 (projection). Statistical analyses, including significance tests, correlation analysis, and Linear Mixed Models (LMM), were employed to evaluate the influence of ADAS design and driver-related factors on SA.

Results indicate that while most SA indicators did not show significant differences between the two ADAS systems, System A consistently demonstrated advantages in visual attention allocation and comprehension. Additionally, driver experience and familiarity with ADAS were found to influence SA, with experienced drivers exhibiting reduced cognitive load and faster responses. A weak correlation between different SA levels suggests that drivers integrate ADAS alerts with external environmental cues.

This study highlights the importance of designing ADAS alerts that provide anticipatory information to enhance driver awareness and decision-making. Future research should expand sample sizes, incorporate additional SA indicators, and explore the long-term effects of ADAS exposure. These findings offer valuable insights for optimizing ADAS signal design to improve road safety and driving performance.

We have successfully designed and implemented a multi-player, multi-modal virtual reality (VR) co-simulator that integrates both pedestrians and cyclists within the same VR environment. This co-simulator is designed to support multi-player, multi-modal VR experiments aimed at comprehensive data collection in transportation research. It features advanced functionalities including body tracking for pedestrians and cyclists, high-quality digital human representations, and comprehensive data collection technologies such as eye gazing data. A VR experiment was conducted to evaluate the effectiveness of the VR co-simulator and investigate the interactions between vulnerable road users (VRUs) and automated vehicles (AVs) in shared spaces. The assessment demonstrated the co-simulator's capabilities and feasibility in terms of simulator sickness, presence, realism, and usability. The experiment also confirmed the impact of VRU combinations and initial relative positions on the interactions between VRUs and AVs in shared space.

his research explores user feedback on proposed mobility solutions in the Merwe-Vierhavens (M4H) area of Rotterdam to inform the development of Community Mobility Hubs (CMHs). Conducted in two phases, the study began with socio-demographic analysis, followed by Virtual Reality (VR) simulations. The initial phase involved desk research to understand the demographic composition and travel patterns in neighborhoods around M4H. Findings revealed a diverse community with a young population (27-39 years old), including European, Turkish, Moroccan migrants, and Dutch non-migrants, primarily in low-income single or family-with children households. Mobility patterns showed varied travel purposes, such as
shopping, commuting, and recreational activities, with walking, cycling, passenger cars, and public transport being the most common modes of transportation. The second phase used VR to provide an immersive experience of the proposed CMH, engaging participants and gathering detailed feedback. Key findings indicated a strong preference for amenities like cafes, co-working spaces, postal services, and refurbishing centers, especially among first- and second-generation migrants. Significant concerns about affordability, reliability, and availability of mobility solutions were also highlighted. Despite limitations such as potential biases in self-reported data and the fixed nature of the VR simulation, the study’s innovative use of VR provided valuable insights. Recommendations for the CMH include creating solutions for diverse demographics, focusing on families, people of migrant backgrounds, and low-income groups, ensuring accessible, affordable, acceptable, and available transport options. The CMH should incorporate practical features to accommodate various activities, address concerns about affordability, availability, and reliability through ongoing community
dialogue, and emphasize convenience, good maintenance, and diverse pricing schemes. Affordable transportation solutions should be offered, targeting user groups most likely to adopt the solutions, such as females and people of migrant backgrounds. Comprehensive services and family-friendly amenities should be included, and community ownership and management encouraged. Both digital and non-digital access points should be provided, and continuous community engagement maintained. Future research should expand the sample size for better representation and include longitudinal studies to track evolving mobility preferences. Enhancing VR simulation quality and addressing potential biases from tech-savvy participants will provide more balanced insights. This research underscores the importance of understanding diverse mobility needs and innovative citizen participation utilizing VR to create inclusive and effective urban mobility solutions for the M4H community.

Electric vehicles (EVs) are required according to the EU legislation from 2014 to install Acoustic Vehicle Alerting Systems (AVAS) to convey information about the EVs through sound signals to other road users, ensuring their safety. Meanwhile,autonomous vehicles (AVs) are also recommended to be equipped with an External Human-Machine Interface (eHMI) system, enabling better interaction with other road users. . However, for the special group of autonomous electric vehicles (AEVs), considering that equipping both eHMI and AVAS, given their overlapping functionality in conveying information, might result in a waste of resources, it remains unclear whether it's feasible to use only eHMI for communication with road users as a cost-saving measure, thereby omitting AVAS or perhaps adding only a simple sound signal to replace AVAS. This study aims to explore whether autonomous electric vehicles need to add extra noise to provide more information to other vulnerable road users, such as cyclists and pedestrians. In this study, we will use Virtual Reality (VR) technology for simulation experiments. Utilizing VR, as opposed to real-world experiments, allows for precise control over the variables in each experiment, ensuring that experiments can be conducted under the same conditions multiple times, thus enhancing reliability. On the other hand, VR can ensure the safety of experiment participants while providing them with an immersive experience, especially in experiments related to traffic safety research.

In this experiment, we will utilize the Unreal Engine to create a simulated testing environment, focusing on observing participants' (acting as cyclists) reactions to a series of variables. These include different environmental noises, the warning distance of sound signals emitted by autonomous vehicles, and the autonomous vehicles themselves. The sound signal warning distance specifically refers to a mechanism where, when pedestrians or cyclists enter a predefined range of the vehicle, it automatically emits a warning signal. This signal is to alert the approaching individuals that the vehicle is in a ready state and may proceed to the next action, thereby enhancing safety interaction and awareness on the road.

The data collected from this experiment will be divided into two parts: first, the participants' reaction times, speed adjustments, and distances obtained from VR devices; second, their perceptions of the trust, perceived safety, and comfort of interactions with autonomous electric vehicles gathered through post-experiment surveys.

The results of this experiment will assist policymakers in refining relevant laws and regulations, as the existing legislation concerning electric vehicles and AVAS do not take autonomous vehicles into account. It will also provide theoretical support for car manufacturers in the design of autonomous electric vehicles.